code: def test_calculate_plot_attention():
      from espnet.nets.pytorch_backend.transformer import plot
  
      train_args = make_train_args(report_cer=True)
      model, feats, feats_len, labels, data, uttid_list = prepare(
          train_args
      )
  
      model.attention_plot_class
      attn_dict = model.calculate_all_attentions(
          feats[0:1], feats_len[0:1], labels[0:1]
      )
  
      plot.plot_multi_head_attention(
          data, uttid_list, attn_dict, "/tmp/espnet-test"
      )
  
imports: import argparse
  import json
  import tempfile
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
label_desc: Use image node_id 8 for calling a global function with example usage: test_calculate_plot_attention() without return types